/**
 * Copyright (c) 2000-2007 Chih-Chung Chang and Chih-Jen Lin
 All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:

1. Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.

3. Neither name of copyright holders nor the names of its contributors
may be used to endorse or promote products derived from this software
without specific prior written permission.


THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE REGENTS OR
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/

package gate.learning.learners.svm;
import java.io.*;
import java.util.*;

//
// Kernel Cache
//
// l is the number of total data items
// size is the cache size limit in bytes
//
class Cache {
	private final int l;
	private int size;
	private final class head_t
	{
		head_t prev, next;	// a cicular list
		float[] data;
		int len;		// data[0,len) is cached in this entry
	}
	private final head_t[] head;
	private head_t lru_head;

	Cache(int l_, int size_)
	{
		l = l_;
		size = size_;
		head = new head_t[l];
		for(int i=0;i<l;i++) head[i] = new head_t();
		size /= 4;
		size -= l * (16/4);	// sizeof(head_t) == 16
		size = Math.max(size, 2*l);  // cache must be large enough for two columns
		lru_head = new head_t();
		lru_head.next = lru_head.prev = lru_head;
	}

	private void lru_delete(head_t h)
	{
		// delete from current location
		h.prev.next = h.next;
		h.next.prev = h.prev;
	}

	private void lru_insert(head_t h)
	{
		// insert to last position
		h.next = lru_head;
		h.prev = lru_head.prev;
		h.prev.next = h;
		h.next.prev = h;
	}

	// request data [0,len)
	// return some position p where [p,len) need to be filled
	// (p >= len if nothing needs to be filled)
	// java: simulate pointer using single-element array
	int get_data(int index, float[][] data, int len)
	{
		head_t h = head[index];
		if(h.len > 0) lru_delete(h);
		int more = len - h.len;

		if(more > 0)
		{
			// free old space
			while(size < more)
			{
				head_t old = lru_head.next;
				lru_delete(old);
				size += old.len;
				old.data = null;
				old.len = 0;
			}

			// allocate new space
			float[] new_data = new float[len];
			if(h.data != null) System.arraycopy(h.data,0,new_data,0,h.len);
			h.data = new_data;
			size -= more;
			do {int _=h.len; h.len=len; len=_;} while(false);
		}

		lru_insert(h);
		data[0] = h.data;
		return len;
	}

	void swap_index(int i, int j)
	{
		if(i==j) return;
		
		if(head[i].len > 0) lru_delete(head[i]);
		if(head[j].len > 0) lru_delete(head[j]);
		do {float[] _=head[i].data; head[i].data=head[j].data; head[j].data=_;} while(false);
		do {int _=head[i].len; head[i].len=head[j].len; head[j].len=_;} while(false);
		if(head[i].len > 0) lru_insert(head[i]);
		if(head[j].len > 0) lru_insert(head[j]);

		if(i>j) do {int _=i; i=j; j=_;} while(false);
		for(head_t h = lru_head.next; h!=lru_head; h=h.next)
		{
			if(h.len > i)
			{
				if(h.len > j)
					do {float _=h.data[i]; h.data[i]=h.data[j]; h.data[j]=_;} while(false);
				else
				{
					// give up
					lru_delete(h);
					size += h.len;
					h.data = null;
					h.len = 0;
				}
			}
		}
	}
}

//
// Kernel evaluation
//
// the static method k_function is for doing single kernel evaluation
// the constructor of Kernel prepares to calculate the l*l kernel matrix
// the member function get_Q is for getting one column from the Q Matrix
//
abstract class QMatrix {
	abstract float[] get_Q(int column, int len);
	abstract float[] get_QD();
	abstract void swap_index(int i, int j);
};

abstract class Kernel extends QMatrix {
	private svm_node[][] x;
	private final double[] x_square;

	// svm_parameter
	private final int kernel_type;
	private final int degree;
	private final double gamma;
	private final double coef0;

	abstract float[] get_Q(int column, int len);
	abstract float[] get_QD();

	void swap_index(int i, int j)
	{
		do {svm_node[] _=x[i]; x[i]=x[j]; x[j]=_;} while(false);
		if(x_square != null) do {double _=x_square[i]; x_square[i]=x_square[j]; x_square[j]=_;} while(false);
	}

	private static double powi(double base, int times)
	{
	        double tmp = base, ret = 1.0;

        	for(int t=times; t>0; t/=2)
		{
                	if(t%2==1) ret*=tmp;
	                tmp = tmp * tmp;
        	}
	        return ret;
	}

	private static double tanh(double x)
	{
		double e = Math.exp(x);
		return 1.0-2.0/(e*e+1);
	}

	double kernel_function(int i, int j)
	{
		switch(kernel_type)
		{
			case svm_parameter.LINEAR:
				return dot(x[i],x[j]);
			case svm_parameter.POLY:
				return powi(gamma*dot(x[i],x[j])+coef0,degree);
			case svm_parameter.RBF:
				return Math.exp(-gamma*(x_square[i]+x_square[j]-2*dot(x[i],x[j])));
			case svm_parameter.SIGMOID:
				return tanh(gamma*dot(x[i],x[j])+coef0);
			case svm_parameter.PRECOMPUTED:
				return x[i][(int)(x[j][0].value)].value;
			default:
				return 0;	// java
		}
	}

	Kernel(int l, svm_node[][] x_, svm_parameter param)
	{
		this.kernel_type = param.kernel_type;
		this.degree = param.degree;
		this.gamma = param.gamma;
		this.coef0 = param.coef0;

		x = (svm_node[][])x_.clone();

		if(kernel_type == svm_parameter.RBF)
		{
			x_square = new double[l];
			for(int i=0;i<l;i++)
				x_square[i] = dot(x[i],x[i]);
		}
		else x_square = null;
	}

	static double dot(svm_node[] x, svm_node[] y)
	{
		double sum = 0;
		int xlen = x.length;
		int ylen = y.length;
		int i = 0;
		int j = 0;
		while(i < xlen && j < ylen)
		{
			if(x[i].index == y[j].index)
				sum += x[i++].value * y[j++].value;
			else
			{
				if(x[i].index > y[j].index)
					++j;
				else
					++i;
			}
		}
		return sum;
	}

	static double k_function(svm_node[] x, svm_node[] y,
					svm_parameter param)
	{
		switch(param.kernel_type)
		{
			case svm_parameter.LINEAR:
				return dot(x,y);
			case svm_parameter.POLY:
				return powi(param.gamma*dot(x,y)+param.coef0,param.degree);
			case svm_parameter.RBF:
			{
				double sum = 0;
				int xlen = x.length;
				int ylen = y.length;
				int i = 0;
				int j = 0;
				while(i < xlen && j < ylen)
				{
					if(x[i].index == y[j].index)
					{
						double d = x[i++].value - y[j++].value;
						sum += d*d;
					}
					else if(x[i].index > y[j].index)
					{
						sum += y[j].value * y[j].value;
						++j;
					}
					else
					{
						sum += x[i].value * x[i].value;
						++i;
					}
				}

				while(i < xlen)
				{
					sum += x[i].value * x[i].value;
					++i;
				}

				while(j < ylen)
				{
					sum += y[j].value * y[j].value;
					++j;
				}

				return Math.exp(-param.gamma*sum);
			}
			case svm_parameter.SIGMOID:
				return tanh(param.gamma*dot(x,y)+param.coef0);
			case svm_parameter.PRECOMPUTED:
				return 	x[(int)(y[0].value)].value;
			default:
				return 0;	// java
		}
	}
}

// Generalized SMO+SVMlight algorithm
// Solves:
//
//	min 0.5(\alpha^T Q \alpha) + b^T \alpha
//
//		y^T \alpha = \delta
//		y_i = +1 or -1
//		0 <= alpha_i <= Cp for y_i = 1
//		0 <= alpha_i <= Cn for y_i = -1
//
// Given:
//
//	Q, b, y, Cp, Cn, and an initial feasible point \alpha
//	l is the size of vectors and matrices
//	eps is the stopping criterion
//
// solution will be put in \alpha, objective value will be put in obj
//
class Solver {
	int active_size;
	byte[] y;
	double[] G;		// gradient of objective function
	static final byte LOWER_BOUND = 0;
	static final byte UPPER_BOUND = 1;
	static final byte FREE = 2;
	byte[] alpha_status;	// LOWER_BOUND, UPPER_BOUND, FREE
	double[] alpha;
	QMatrix Q;
	float[] QD;
	double eps;
	double Cp,Cn;
	double[] b;
	int[] active_set;
	double[] G_bar;		// gradient, if we treat free variables as 0
	int l;
	boolean unshrinked;	// XXX
	
	static final double INF = java.lang.Double.POSITIVE_INFINITY;

	double get_C(int i)
	{
		return (y[i] > 0)? Cp : Cn;
	}
	void update_alpha_status(int i)
	{
		if(alpha[i] >= get_C(i))
			alpha_status[i] = UPPER_BOUND;
		else if(alpha[i] <= 0)
			alpha_status[i] = LOWER_BOUND;
		else alpha_status[i] = FREE;
	}
	boolean is_upper_bound(int i) { return alpha_status[i] == UPPER_BOUND; }
	boolean is_lower_bound(int i) { return alpha_status[i] == LOWER_BOUND; }
	boolean is_free(int i) {  return alpha_status[i] == FREE; }

	// java: information about solution except alpha,
	// because we cannot return multiple values otherwise...
	static class SolutionInfo {
		double obj;
		double rho;
		double upper_bound_p;
		double upper_bound_n;
		double r;	// for Solver_NU
	}

	void swap_index(int i, int j)
	{
		Q.swap_index(i,j);
		do {byte _=y[i]; y[i]=y[j]; y[j]=_;} while(false);
		do {double _=G[i]; G[i]=G[j]; G[j]=_;} while(false);
		do {byte _=alpha_status[i]; alpha_status[i]=alpha_status[j]; alpha_status[j]=_;} while(false);
		do {double _=alpha[i]; alpha[i]=alpha[j]; alpha[j]=_;} while(false);
		do {double _=b[i]; b[i]=b[j]; b[j]=_;} while(false);
		do {int _=active_set[i]; active_set[i]=active_set[j]; active_set[j]=_;} while(false);
		do {double _=G_bar[i]; G_bar[i]=G_bar[j]; G_bar[j]=_;} while(false);
	}

	void reconstruct_gradient()
	{
		// reconstruct inactive elements of G from G_bar and free variables

		if(active_size == l) return;

		int i;
		for(i=active_size;i<l;i++)
			G[i] = G_bar[i] + b[i];

		for(i=0;i<active_size;i++)
			if(is_free(i))
			{
				float[] Q_i = Q.get_Q(i,l);
				double alpha_i = alpha[i];
				for(int j=active_size;j<l;j++)
					G[j] += alpha_i * Q_i[j];
			}
	}

	void Solve(int l, QMatrix Q, double[] b_, byte[] y_,
		   double[] alpha_, double Cp, double Cn, double eps, SolutionInfo si, int shrinking)
	{
		this.l = l;
		this.Q = Q;
		QD = Q.get_QD();
		b = (double[])b_.clone();
		y = (byte[])y_.clone();
		alpha = (double[])alpha_.clone();
		this.Cp = Cp;
		this.Cn = Cn;
		this.eps = eps;
		this.unshrinked = false;

		// initialize alpha_status
		{
			alpha_status = new byte[l];
			for(int i=0;i<l;i++)
				update_alpha_status(i);
		}

		// initialize active set (for shrinking)
		{
			active_set = new int[l];
			for(int i=0;i<l;i++)
				active_set[i] = i;
			active_size = l;
		}

		// initialize gradient
		{
			G = new double[l];
			G_bar = new double[l];
			int i;
			for(i=0;i<l;i++)
			{
				G[i] = b[i];
				G_bar[i] = 0;
			}
			for(i=0;i<l;i++)
				if(!is_lower_bound(i))
				{
					float[] Q_i = Q.get_Q(i,l);
					double alpha_i = alpha[i];
					int j;
					for(j=0;j<l;j++)
						G[j] += alpha_i*Q_i[j];
					if(is_upper_bound(i))
						for(j=0;j<l;j++)
							G_bar[j] += get_C(i) * Q_i[j];
				}
		}

		// optimization step

		int iter = 0;
		int counter = Math.min(l,1000)+1;
		int[] working_set = new int[2];

		while(true)
		{
			// show progress and do shrinking

			if(--counter == 0)
			{
				counter = Math.min(l,1000);
				if(shrinking!=0) do_shrinking();
				//System.err.print(".");
			}

			if(select_working_set(working_set)!=0)
			{
				// reconstruct the whole gradient
				reconstruct_gradient();
				// reset active set size and check
				active_size = l;
				//System.err.print("*");
				if(select_working_set(working_set)!=0)
					break;
				else
					counter = 1;	// do shrinking next iteration
			}
			
			int i = working_set[0];
			int j = working_set[1];

			++iter;

			// update alpha[i] and alpha[j], handle bounds carefully

			float[] Q_i = Q.get_Q(i,active_size);
			float[] Q_j = Q.get_Q(j,active_size);

			double C_i = get_C(i);
			double C_j = get_C(j);

			double old_alpha_i = alpha[i];
			double old_alpha_j = alpha[j];

			if(y[i]!=y[j])
			{
				double quad_coef = Q_i[i]+Q_j[j]+2*Q_i[j];
				if (quad_coef <= 0)
					quad_coef = 1e-12;
				double delta = (-G[i]-G[j])/quad_coef;
				double diff = alpha[i] - alpha[j];
				alpha[i] += delta;
				alpha[j] += delta;
			
				if(diff > 0)
				{
					if(alpha[j] < 0)
					{
						alpha[j] = 0;
						alpha[i] = diff;
					}
				}
				else
				{
					if(alpha[i] < 0)
					{
						alpha[i] = 0;
						alpha[j] = -diff;
					}
				}
				if(diff > C_i - C_j)
				{
					if(alpha[i] > C_i)
					{
						alpha[i] = C_i;
						alpha[j] = C_i - diff;
					}
				}
				else
				{
					if(alpha[j] > C_j)
					{
						alpha[j] = C_j;
						alpha[i] = C_j + diff;
					}
				}
			}
			else
			{
				double quad_coef = Q_i[i]+Q_j[j]-2*Q_i[j];
				if (quad_coef <= 0)
					quad_coef = 1e-12;
				double delta = (G[i]-G[j])/quad_coef;
				double sum = alpha[i] + alpha[j];
				alpha[i] -= delta;
				alpha[j] += delta;

				if(sum > C_i)
				{
					if(alpha[i] > C_i)
					{
						alpha[i] = C_i;
						alpha[j] = sum - C_i;
					}
				}
				else
				{
					if(alpha[j] < 0)
					{
						alpha[j] = 0;
						alpha[i] = sum;
					}
				}
				if(sum > C_j)
				{
					if(alpha[j] > C_j)
					{
						alpha[j] = C_j;
						alpha[i] = sum - C_j;
					}
				}
				else
				{
					if(alpha[i] < 0)
					{
						alpha[i] = 0;
						alpha[j] = sum;
					}
				}
			}

			// update G

			double delta_alpha_i = alpha[i] - old_alpha_i;
			double delta_alpha_j = alpha[j] - old_alpha_j;

			for(int k=0;k<active_size;k++)
			{
				G[k] += Q_i[k]*delta_alpha_i + Q_j[k]*delta_alpha_j;
			}

			// update alpha_status and G_bar

			{
				boolean ui = is_upper_bound(i);
				boolean uj = is_upper_bound(j);
				update_alpha_status(i);
				update_alpha_status(j);
				int k;
				if(ui != is_upper_bound(i))
				{
					Q_i = Q.get_Q(i,l);
					if(ui)
						for(k=0;k<l;k++)
							G_bar[k] -= C_i * Q_i[k];
					else
						for(k=0;k<l;k++)
							G_bar[k] += C_i * Q_i[k];
				}

				if(uj != is_upper_bound(j))
				{
					Q_j = Q.get_Q(j,l);
					if(uj)
						for(k=0;k<l;k++)
							G_bar[k] -= C_j * Q_j[k];
					else
						for(k=0;k<l;k++)
							G_bar[k] += C_j * Q_j[k];
				}
			}

		}

		// calculate rho

		si.rho = calculate_rho();

		// calculate objective value
		{
			double v = 0;
			int i;
			for(i=0;i<l;i++)
				v += alpha[i] * (G[i] + b[i]);

			si.obj = v/2;
		}

		// put back the solution
		{
			for(int i=0;i<l;i++)
				alpha_[active_set[i]] = alpha[i];
		}

		si.upper_bound_p = Cp;
		si.upper_bound_n = Cn;

		//System.out.print("\noptimization finished, #iter = "+iter+"\n");
	}

	// return 1 if already optimal, return 0 otherwise
	int select_working_set(int[] working_set)
	{
		// return i,j such that
		// i: maximizes -y_i * grad(f)_i, i in I_up(\alpha)
		// j: mimimizes the decrease of obj value
		//    (if quadratic coefficeint <= 0, replace it with tau)
		//    -y_j*grad(f)_j < -y_i*grad(f)_i, j in I_low(\alpha)
		
		double Gmax = -INF;
		double Gmax2 = -INF;
		int Gmax_idx = -1;
		int Gmin_idx = -1;
		double obj_diff_min = INF;
	
		for(int t=0;t<active_size;t++)
			if(y[t]==+1)	
			{
				if(!is_upper_bound(t))
					if(-G[t] >= Gmax)
					{
						Gmax = -G[t];
						Gmax_idx = t;
					}
			}
			else
			{
				if(!is_lower_bound(t))
					if(G[t] >= Gmax)
					{
						Gmax = G[t];
						Gmax_idx = t;
					}
			}
	
		int i = Gmax_idx;
		float[] Q_i = null;
		if(i != -1) // null Q_i not accessed: Gmax=-INF if i=-1
			Q_i = Q.get_Q(i,active_size);
	
		for(int j=0;j<active_size;j++)
		{
			if(y[j]==+1)
			{
				if (!is_lower_bound(j))
				{
					double grad_diff=Gmax+G[j];
					if (G[j] >= Gmax2)
						Gmax2 = G[j];
					if (grad_diff > 0)
					{
						double obj_diff; 
						double quad_coef=Q_i[i]+QD[j]-2*y[i]*Q_i[j];
						if (quad_coef > 0)
							obj_diff = -(grad_diff*grad_diff)/quad_coef;
						else
							obj_diff = -(grad_diff*grad_diff)/1e-12;
	
						if (obj_diff <= obj_diff_min)
						{
							Gmin_idx=j;
							obj_diff_min = obj_diff;
						}
					}
				}
			}
			else
			{
				if (!is_upper_bound(j))
				{
					double grad_diff= Gmax-G[j];
					if (-G[j] >= Gmax2)
						Gmax2 = -G[j];
					if (grad_diff > 0)
					{
						double obj_diff; 
						double quad_coef=Q_i[i]+QD[j]+2*y[i]*Q_i[j];
						if (quad_coef > 0)
							obj_diff = -(grad_diff*grad_diff)/quad_coef;
						else
							obj_diff = -(grad_diff*grad_diff)/1e-12;
	
						if (obj_diff <= obj_diff_min)
						{
							Gmin_idx=j;
							obj_diff_min = obj_diff;
						}
					}
				}
			}
		}

		if(Gmax+Gmax2 < eps)
			return 1;

		working_set[0] = Gmax_idx;
		working_set[1] = Gmin_idx;
		return 0;
	}

	// return 1 if already optimal, return 0 otherwise
	int max_violating_pair(int[] working_set)
	{
		// return i,j which maximize -grad(f)^T d , under constraint
		// if alpha_i == C, d != +1
		// if alpha_i == 0, d != -1

		double Gmax1 = -INF;		// max { -y_i * grad(f)_i | i in I_up(\alpha) }
		int Gmax1_idx = -1;

		int Gmax2_idx = -1;
		double Gmax2 = -INF;		// max { y_i * grad(f)_i | i in I_low(\alpha) }

		for(int i=0;i<active_size;i++)
		{
			if(y[i]==+1)	// y = +1
			{
				if(!is_upper_bound(i))	// d = +1
				{
					if(-G[i] >= Gmax1)
					{
						Gmax1 = -G[i];
						Gmax1_idx = i;
					}
				}
				if(!is_lower_bound(i))	// d = -1
				{
					if(G[i] >= Gmax2)
					{
						Gmax2 = G[i];
						Gmax2_idx = i;
					}
				}
			}
			else		// y = -1
			{
				if(!is_upper_bound(i))	// d = +1
				{
					if(-G[i] >= Gmax2)
					{
						Gmax2 = -G[i];
						Gmax2_idx = i;
					}
				}
				if(!is_lower_bound(i))	// d = -1
				{
					if(G[i] >= Gmax1)
					{
						Gmax1 = G[i];
						Gmax1_idx = i;
					}
				}
			}
		}

		if(Gmax1+Gmax2 < eps)
	 		return 1;

		working_set[0] = Gmax1_idx;
		working_set[1] = Gmax2_idx;
		return 0;
	}

	void do_shrinking()
	{
		int i,j,k;
		int[] working_set = new int[2];
		if(max_violating_pair(working_set)!=0) return;
		i = working_set[0];
		j = working_set[1];
		double Gm1 = -y[j]*G[j];
		double Gm2 = y[i]*G[i];

		// shrink
	
		for(k=0;k<active_size;k++)
		{
			if(is_lower_bound(k))
			{
				if(y[k]==+1)
				{
					if(-G[k] >= Gm1) continue;
				}
				else	if(-G[k] >= Gm2) continue;
			}
			else if(is_upper_bound(k))
			{
				if(y[k]==+1)
				{
					if(G[k] >= Gm2) continue;
				}
				else	if(G[k] >= Gm1) continue;
			}
			else continue;

			--active_size;
			swap_index(k,active_size);
			--k;	// look at the newcomer
		}

		// unshrink, check all variables again before final iterations

		if(unshrinked || -(Gm1 + Gm2) > eps*10) return;

		unshrinked = true;
		reconstruct_gradient();

		for(k=l-1;k>=active_size;k--)
		{
			if(is_lower_bound(k))
			{
				if(y[k]==+1)
				{
					if(-G[k] < Gm1) continue;
				}
				else	if(-G[k] < Gm2) continue;
			}
			else if(is_upper_bound(k))
			{
				if(y[k]==+1)
				{
					if(G[k] < Gm2) continue;
				}
				else	if(G[k] < Gm1) continue;
			}
			else continue;

			swap_index(k,active_size);
			active_size++;
			++k;	// look at the newcomer
		}
	}

	double calculate_rho()
	{
		double r;
		int nr_free = 0;
		double ub = INF, lb = -INF, sum_free = 0;
		for(int i=0;i<active_size;i++)
		{
			double yG = y[i]*G[i];

			if(is_lower_bound(i))
			{
				if(y[i] > 0)
					ub = Math.min(ub,yG);
				else
					lb = Math.max(lb,yG);
			}
			else if(is_upper_bound(i))
			{
				if(y[i] < 0)
					ub = Math.min(ub,yG);
				else
					lb = Math.max(lb,yG);
			}
			else
			{
				++nr_free;
				sum_free += yG;
			}
		}

		if(nr_free>0)
			r = sum_free/nr_free;
		else
			r = (ub+lb)/2;

		return r;
	}

}

//
// Solver for nu-svm classification and regression
//
// additional constraint: e^T \alpha = constant
//
final class Solver_NU extends Solver
{
	private SolutionInfo si;

	void Solve(int l, QMatrix Q, double[] b, byte[] y,
		   double[] alpha, double Cp, double Cn, double eps,
		   SolutionInfo si, int shrinking)
	{
		this.si = si;
		super.Solve(l,Q,b,y,alpha,Cp,Cn,eps,si,shrinking);
	}

	// return 1 if already optimal, return 0 otherwise
	int select_working_set(int[] working_set)
	{
		// return i,j such that y_i = y_j and
		// i: maximizes -y_i * grad(f)_i, i in I_up(\alpha)
		// j: minimizes the decrease of obj value
		//    (if quadratic coefficeint <= 0, replace it with tau)
		//    -y_j*grad(f)_j < -y_i*grad(f)_i, j in I_low(\alpha)
	
		double Gmaxp = -INF;
		double Gmaxp2 = -INF;
		int Gmaxp_idx = -1;
	
		double Gmaxn = -INF;
		double Gmaxn2 = -INF;
		int Gmaxn_idx = -1;
	
		int Gmin_idx = -1;
		double obj_diff_min = INF;
	
		for(int t=0;t<active_size;t++)
			if(y[t]==+1)
			{
				if(!is_upper_bound(t))
					if(-G[t] >= Gmaxp)
					{
						Gmaxp = -G[t];
						Gmaxp_idx = t;
					}
			}
			else
			{
				if(!is_lower_bound(t))
					if(G[t] >= Gmaxn)
					{
						Gmaxn = G[t];
						Gmaxn_idx = t;
					}
			}
	
		int ip = Gmaxp_idx;
		int in = Gmaxn_idx;
		float[] Q_ip = null;
		float[] Q_in = null;
		if(ip != -1) // null Q_ip not accessed: Gmaxp=-INF if ip=-1
			Q_ip = Q.get_Q(ip,active_size);
		if(in != -1)
			Q_in = Q.get_Q(in,active_size);
	
		for(int j=0;j<active_size;j++)
		{
			if(y[j]==+1)
			{
				if (!is_lower_bound(j))	
				{
					double grad_diff=Gmaxp+G[j];
					if (G[j] >= Gmaxp2)
						Gmaxp2 = G[j];
					if (grad_diff > 0)
					{
						double obj_diff; 
						double quad_coef = Q_ip[ip]+QD[j]-2*Q_ip[j];
						if (quad_coef > 0)
							obj_diff = -(grad_diff*grad_diff)/quad_coef;
						else
							obj_diff = -(grad_diff*grad_diff)/1e-12;
	
						if (obj_diff <= obj_diff_min)
						{
							Gmin_idx=j;
							obj_diff_min = obj_diff;
						}
					}
				}
			}
			else
			{
				if (!is_upper_bound(j))
				{
					double grad_diff=Gmaxn-G[j];
					if (-G[j] >= Gmaxn2)
						Gmaxn2 = -G[j];
					if (grad_diff > 0)
					{
						double obj_diff; 
						double quad_coef = Q_in[in]+QD[j]-2*Q_in[j];
						if (quad_coef > 0)
							obj_diff = -(grad_diff*grad_diff)/quad_coef;
						else
							obj_diff = -(grad_diff*grad_diff)/1e-12;
	
						if (obj_diff <= obj_diff_min)
						{
							Gmin_idx=j;
							obj_diff_min = obj_diff;
						}
					}
				}
			}
		}

		if(Math.max(Gmaxp+Gmaxp2,Gmaxn+Gmaxn2) < eps)
 			return 1;
	
		if(y[Gmin_idx] == +1)
			working_set[0] = Gmaxp_idx;
		else
			working_set[0] = Gmaxn_idx;
		working_set[1] = Gmin_idx;
	
		return 0;
	}

	void do_shrinking()
	{
		double Gmax1 = -INF;	// max { -y_i * grad(f)_i | y_i = +1, i in I_up(\alpha) }
		double Gmax2 = -INF;	// max { y_i * grad(f)_i | y_i = +1, i in I_low(\alpha) }
		double Gmax3 = -INF;	// max { -y_i * grad(f)_i | y_i = -1, i in I_up(\alpha) }
		double Gmax4 = -INF;	// max { y_i * grad(f)_i | y_i = -1, i in I_low(\alpha) }
 
		// find maximal violating pair first
		int k;
		for(k=0;k<active_size;k++)
		{
			if(!is_upper_bound(k))
			{
				if(y[k]==+1)
				{
					if(-G[k] > Gmax1) Gmax1 = -G[k];
				}
				else	if(-G[k] > Gmax3) Gmax3 = -G[k];
			}
			if(!is_lower_bound(k))
			{
				if(y[k]==+1)
				{	
					if(G[k] > Gmax2) Gmax2 = G[k];
				}
				else	if(G[k] > Gmax4) Gmax4 = G[k];
			}
		}

		// shrinking

		double Gm1 = -Gmax2;
		double Gm2 = -Gmax1;
		double Gm3 = -Gmax4;
		double Gm4 = -Gmax3;

		for(k=0;k<active_size;k++)
		{
			if(is_lower_bound(k))
			{
				if(y[k]==+1)
				{
					if(-G[k] >= Gm1) continue;
				}
				else	if(-G[k] >= Gm3) continue;
			}
			else if(is_upper_bound(k))
			{
				if(y[k]==+1)
				{
					if(G[k] >= Gm2) continue;
				}
				else	if(G[k] >= Gm4) continue;
			}
			else continue;

			--active_size;
			swap_index(k,active_size);
			--k;	// look at the newcomer
		}

		// unshrink, check all variables again before final iterations

		if(unshrinked || Math.max(-(Gm1+Gm2),-(Gm3+Gm4)) > eps*10) return;
	
		unshrinked = true;
		reconstruct_gradient();

		for(k=l-1;k>=active_size;k--)
		{
			if(is_lower_bound(k))
			{
				if(y[k]==+1)
				{
					if(-G[k] < Gm1) continue;
				}
				else	if(-G[k] < Gm3) continue;
			}
			else if(is_upper_bound(k))
			{
				if(y[k]==+1)
				{
					if(G[k] < Gm2) continue;
				}
				else	if(G[k] < Gm4) continue;
			}
			else continue;

			swap_index(k,active_size);
			active_size++;
			++k;	// look at the newcomer
		}
	}
	
	double calculate_rho()
	{
		int nr_free1 = 0,nr_free2 = 0;
		double ub1 = INF, ub2 = INF;
		double lb1 = -INF, lb2 = -INF;
		double sum_free1 = 0, sum_free2 = 0;

		for(int i=0;i<active_size;i++)
		{
			if(y[i]==+1)
			{
				if(is_lower_bound(i))
					ub1 = Math.min(ub1,G[i]);
				else if(is_upper_bound(i))
					lb1 = Math.max(lb1,G[i]);
				else
				{
					++nr_free1;
					sum_free1 += G[i];
				}
			}
			else
			{
				if(is_lower_bound(i))
					ub2 = Math.min(ub2,G[i]);
				else if(is_upper_bound(i))
					lb2 = Math.max(lb2,G[i]);
				else
				{
					++nr_free2;
					sum_free2 += G[i];
				}
			}
		}

		double r1,r2;
		if(nr_free1 > 0)
			r1 = sum_free1/nr_free1;
		else
			r1 = (ub1+lb1)/2;

		if(nr_free2 > 0)
			r2 = sum_free2/nr_free2;
		else
			r2 = (ub2+lb2)/2;

		si.r = (r1+r2)/2;
		return (r1-r2)/2;
	}
}

//
// Q matrices for various formulations
//
class SVC_Q extends Kernel
{
	private final byte[] y;
	private final Cache cache;
	private final float[] QD;

	SVC_Q(svm_problem prob, svm_parameter param, byte[] y_)
	{
		super(prob.l, prob.x, param);
		y = (byte[])y_.clone();
		cache = new Cache(prob.l,(int)(param.cache_size*(1<<20)));
		QD = new float[prob.l];
		for(int i=0;i<prob.l;i++)
			QD[i]= (float)kernel_function(i,i);
	}

	float[] get_Q(int i, int len)
	{
		float[][] data = new float[1][];
		int start;
		if((start = cache.get_data(i,data,len)) < len)
		{
			for(int j=start;j<len;j++)
				data[0][j] = (float)(y[i]*y[j]*kernel_function(i,j));
		}
		return data[0];
	}

	float[] get_QD()
	{
		return QD;
	}

	void swap_index(int i, int j)
	{
		cache.swap_index(i,j);
		super.swap_index(i,j);
		do {byte _=y[i]; y[i]=y[j]; y[j]=_;} while(false);
		do {float _=QD[i]; QD[i]=QD[j]; QD[j]=_;} while(false);
	}
}

class ONE_CLASS_Q extends Kernel
{
	private final Cache cache;
	private final float[] QD;

	ONE_CLASS_Q(svm_problem prob, svm_parameter param)
	{
		super(prob.l, prob.x, param);
		cache = new Cache(prob.l,(int)(param.cache_size*(1<<20)));
		QD = new float[prob.l];
		for(int i=0;i<prob.l;i++)
			QD[i]= (float)kernel_function(i,i);
	}

	float[] get_Q(int i, int len)
	{
		float[][] data = new float[1][];
		int start;
		if((start = cache.get_data(i,data,len)) < len)
		{
			for(int j=start;j<len;j++)
				data[0][j] = (float)kernel_function(i,j);
		}
		return data[0];
	}

	float[] get_QD()
	{
		return QD;
	}

	void swap_index(int i, int j)
	{
		cache.swap_index(i,j);
		super.swap_index(i,j);
		do {float _=QD[i]; QD[i]=QD[j]; QD[j]=_;} while(false);
	}
}

class SVR_Q extends Kernel
{
	private final int l;
	private final Cache cache;
	private final byte[] sign;
	private final int[] index;
	private int next_buffer;
	private float[][] buffer;
	private final float[] QD;

	SVR_Q(svm_problem prob, svm_parameter param)
	{
		super(prob.l, prob.x, param);
		l = prob.l;
		cache = new Cache(l,(int)(param.cache_size*(1<<20)));
		QD = new float[2*l];
		sign = new byte[2*l];
		index = new int[2*l];
		for(int k=0;k<l;k++)
		{
			sign[k] = 1;
			sign[k+l] = -1;
			index[k] = k;
			index[k+l] = k;
			QD[k] = (float)kernel_function(k,k);
			QD[k+l] = QD[k];
		}
		buffer = new float[2][2*l];
		next_buffer = 0;
	}

	void swap_index(int i, int j)
	{
		do {byte _=sign[i]; sign[i]=sign[j]; sign[j]=_;} while(false);
		do {int _=index[i]; index[i]=index[j]; index[j]=_;} while(false);
		do {float _=QD[i]; QD[i]=QD[j]; QD[j]=_;} while(false);
	}

	float[] get_Q(int i, int len)
	{
		float[][] data = new float[1][];
		int real_i = index[i];
		if(cache.get_data(real_i,data,l) < l)
		{
			for(int j=0;j<l;j++)
				data[0][j] = (float)kernel_function(real_i,j);
		}

		// reorder and copy
		float buf[] = buffer[next_buffer];
		next_buffer = 1 - next_buffer;
		byte si = sign[i];
		for(int j=0;j<len;j++)
			buf[j] = si * sign[j] * data[0][index[j]];
		return buf;
	}

	float[] get_QD()
	{
		return QD;
	}
}

public class svm {
	//
	// construct and solve various formulations
	//
	private static void solve_c_svc(svm_problem prob, svm_parameter param,
					double[] alpha, Solver.SolutionInfo si,
					double Cp, double Cn)
	{
		int l = prob.l;
		double[] minus_ones = new double[l];
		byte[] y = new byte[l];

		int i;

		for(i=0;i<l;i++)
		{
			alpha[i] = 0;
			minus_ones[i] = -1;
			if(prob.y[i] > 0) y[i] = +1; else y[i]=-1;
		}

		Solver s = new Solver();
		s.Solve(l, new SVC_Q(prob,param,y), minus_ones, y,
			alpha, Cp, Cn, param.eps, si, param.shrinking);

		double sum_alpha=0;
		for(i=0;i<l;i++)
			sum_alpha += alpha[i];

		//if (Cp==Cn)
			//System.out.print("nu = "+sum_alpha/(Cp*prob.l)+"\n");

		for(i=0;i<l;i++)
			alpha[i] *= y[i];
	}

	private static void solve_nu_svc(svm_problem prob, svm_parameter param,
				 	double[] alpha, Solver.SolutionInfo si)
	{
		int i;
		int l = prob.l;
		double nu = param.nu;

		byte[] y = new byte[l];

		for(i=0;i<l;i++)
			if(prob.y[i]>0)
				y[i] = +1;
			else
				y[i] = -1;

		double sum_pos = nu*l/2;
		double sum_neg = nu*l/2;

		for(i=0;i<l;i++)
			if(y[i] == +1)
			{
				alpha[i] = Math.min(1.0,sum_pos);
				sum_pos -= alpha[i];
			}
			else
			{
				alpha[i] = Math.min(1.0,sum_neg);
				sum_neg -= alpha[i];
			}

		double[] zeros = new double[l];

		for(i=0;i<l;i++)
			zeros[i] = 0;

		Solver_NU s = new Solver_NU();
		s.Solve(l, new SVC_Q(prob,param,y), zeros, y,
			alpha, 1.0, 1.0, param.eps, si, param.shrinking);
		double r = si.r;

		System.out.print("C = "+1/r+"\n");

		for(i=0;i<l;i++)
			alpha[i] *= y[i]/r;

		si.rho /= r;
		si.obj /= (r*r);
		si.upper_bound_p = 1/r;
		si.upper_bound_n = 1/r;
	}

	private static void solve_one_class(svm_problem prob, svm_parameter param,
				    	double[] alpha, Solver.SolutionInfo si)
	{
		int l = prob.l;
		double[] zeros = new double[l];
		byte[] ones = new byte[l];
		int i;

		int n = (int)(param.nu*prob.l);	// # of alpha's at upper bound

		for(i=0;i<n;i++)
			alpha[i] = 1;
		if(n<prob.l)
			alpha[n] = param.nu * prob.l - n;
		for(i=n+1;i<l;i++)
			alpha[i] = 0;

		for(i=0;i<l;i++)
		{
			zeros[i] = 0;
			ones[i] = 1;
		}

		Solver s = new Solver();
		s.Solve(l, new ONE_CLASS_Q(prob,param), zeros, ones,
			alpha, 1.0, 1.0, param.eps, si, param.shrinking);
	}

	private static void solve_epsilon_svr(svm_problem prob, svm_parameter param,
					double[] alpha, Solver.SolutionInfo si)
	{
		int l = prob.l;
		double[] alpha2 = new double[2*l];
		double[] linear_term = new double[2*l];
		byte[] y = new byte[2*l];
		int i;

		for(i=0;i<l;i++)
		{
			alpha2[i] = 0;
			linear_term[i] = param.p - prob.y[i];
			y[i] = 1;

			alpha2[i+l] = 0;
			linear_term[i+l] = param.p + prob.y[i];
			y[i+l] = -1;
		}

		Solver s = new Solver();
		s.Solve(2*l, new SVR_Q(prob,param), linear_term, y,
			alpha2, param.C, param.C, param.eps, si, param.shrinking);

		double sum_alpha = 0;
		for(i=0;i<l;i++)
		{
			alpha[i] = alpha2[i] - alpha2[i+l];
			sum_alpha += Math.abs(alpha[i]);
		}
		System.out.print("nu = "+sum_alpha/(param.C*l)+"\n");
	}

	private static void solve_nu_svr(svm_problem prob, svm_parameter param,
					double[] alpha, Solver.SolutionInfo si)
	{
		int l = prob.l;
		double C = param.C;
		double[] alpha2 = new double[2*l];
		double[] linear_term = new double[2*l];
		byte[] y = new byte[2*l];
		int i;

		double sum = C * param.nu * l / 2;
		for(i=0;i<l;i++)
		{
			alpha2[i] = alpha2[i+l] = Math.min(sum,C);
			sum -= alpha2[i];
			
			linear_term[i] = - prob.y[i];
			y[i] = 1;

			linear_term[i+l] = prob.y[i];
			y[i+l] = -1;
		}

		Solver_NU s = new Solver_NU();
		s.Solve(2*l, new SVR_Q(prob,param), linear_term, y,
			alpha2, C, C, param.eps, si, param.shrinking);

		System.out.print("epsilon = "+(-si.r)+"\n");
		
		for(i=0;i<l;i++)
			alpha[i] = alpha2[i] - alpha2[i+l];
	}

	//
	// decision_function
	//
	public static class decision_function
	{
		public double[] alpha;
		public double rho;	
	};

	public static decision_function svm_train_one(
		svm_problem prob, svm_parameter param,
		double Cp, double Cn)
	{
		double[] alpha = new double[prob.l];
		Solver.SolutionInfo si = new Solver.SolutionInfo();
		switch(param.svm_type)
		{
			case svm_parameter.C_SVC:
				solve_c_svc(prob,param,alpha,si,Cp,Cn);
				break;
			case svm_parameter.NU_SVC:
				solve_nu_svc(prob,param,alpha,si);
				break;
			case svm_parameter.ONE_CLASS:
				solve_one_class(prob,param,alpha,si);
				break;
			case svm_parameter.EPSILON_SVR:
				solve_epsilon_svr(prob,param,alpha,si);
				break;
			case svm_parameter.NU_SVR:
				solve_nu_svr(prob,param,alpha,si);
				break;
		}

		//System.out.print("obj = "+si.obj+", rho = "+si.rho+"\n");

		// output SVs

		int nSV = 0;
		int nBSV = 0;
		for(int i=0;i<prob.l;i++)
		{
			if(Math.abs(alpha[i]) > 0)
			{
				++nSV;
				if(prob.y[i] > 0)
				{
					if(Math.abs(alpha[i]) >= si.upper_bound_p)
					++nBSV;
				}
				else
				{
					if(Math.abs(alpha[i]) >= si.upper_bound_n)
						++nBSV;
				}
			}
		}

		//System.out.print("nSV = "+nSV+", nBSV = "+nBSV+"\n");

		decision_function f = new decision_function();
		f.alpha = alpha;
		f.rho = si.rho;
		return f;
	}

	// Platt's binary SVM Probablistic Output: an improvement from Lin et al.
	private static void sigmoid_train(int l, double[] dec_values, double[] labels, 
				  double[] probAB)
	{
		double A, B;
		double prior1=0, prior0 = 0;
		int i;

		for (i=0;i<l;i++)
			if (labels[i] > 0) prior1+=1;
			else prior0+=1;
	
		int max_iter=100; 	// Maximal number of iterations
		double min_step=1e-10;	// Minimal step taken in line search
		double sigma=1e-3;	// For numerically strict PD of Hessian
		double eps=1e-5;
		double hiTarget=(prior1+1.0)/(prior1+2.0);
		double loTarget=1/(prior0+2.0);
		double[] t= new double[l];
		double fApB,p,q,h11,h22,h21,g1,g2,det,dA,dB,gd,stepsize;
		double newA,newB,newf,d1,d2;
		int iter; 
	
		// Initial Point and Initial Fun Value
		A=0.0; B=Math.log((prior0+1.0)/(prior1+1.0));
		double fval = 0.0;

		for (i=0;i<l;i++)
		{
			if (labels[i]>0) t[i]=hiTarget;
			else t[i]=loTarget;
			fApB = dec_values[i]*A+B;
			if (fApB>=0)
				fval += t[i]*fApB + Math.log(1+Math.exp(-fApB));
			else
				fval += (t[i] - 1)*fApB +Math.log(1+Math.exp(fApB));
		}
		for (iter=0;iter<max_iter;iter++)
		{
			// Update Gradient and Hessian (use H' = H + sigma I)
			h11=sigma; // numerically ensures strict PD
			h22=sigma;
			h21=0.0;g1=0.0;g2=0.0;
			for (i=0;i<l;i++)
			{
				fApB = dec_values[i]*A+B;
				if (fApB >= 0)
				{
					p=Math.exp(-fApB)/(1.0+Math.exp(-fApB));
					q=1.0/(1.0+Math.exp(-fApB));
				}
				else
				{
					p=1.0/(1.0+Math.exp(fApB));
					q=Math.exp(fApB)/(1.0+Math.exp(fApB));
				}
				d2=p*q;
				h11+=dec_values[i]*dec_values[i]*d2;
				h22+=d2;
				h21+=dec_values[i]*d2;
				d1=t[i]-p;
				g1+=dec_values[i]*d1;
				g2+=d1;
			}

			// Stopping Criteria
			if (Math.abs(g1)<eps && Math.abs(g2)<eps)
				break;
			
			// Finding Newton direction: -inv(H') * g
			det=h11*h22-h21*h21;
			dA=-(h22*g1 - h21 * g2) / det;
			dB=-(-h21*g1+ h11 * g2) / det;
			gd=g1*dA+g2*dB;


			stepsize = 1; 		// Line Search
			while (stepsize >= min_step)
			{
				newA = A + stepsize * dA;
				newB = B + stepsize * dB;

				// New function value
				newf = 0.0;
				for (i=0;i<l;i++)
				{
					fApB = dec_values[i]*newA+newB;
					if (fApB >= 0)
						newf += t[i]*fApB + Math.log(1+Math.exp(-fApB));
					else
						newf += (t[i] - 1)*fApB +Math.log(1+Math.exp(fApB));
				}
				// Check sufficient decrease
				if (newf<fval+0.0001*stepsize*gd)
				{
					A=newA;B=newB;fval=newf;
					break;
				}
				else
					stepsize = stepsize / 2.0;
			}
			
			if (stepsize < min_step)
			{
				System.err.print("Line search fails in two-class probability estimates\n");
				break;
			}
		}
		
		if (iter>=max_iter)
			System.err.print("Reaching maximal iterations in two-class probability estimates\n");
		probAB[0]=A;probAB[1]=B;
	}

	private static double sigmoid_predict(double decision_value, double A, double B)
	{
		double fApB = decision_value*A+B;
		if (fApB >= 0)
			return Math.exp(-fApB)/(1.0+Math.exp(-fApB));
		else
			return 1.0/(1+Math.exp(fApB)) ;
	}

	// Method 2 from the multiclass_prob paper by Wu, Lin, and Weng
	private static void multiclass_probability(int k, double[][] r, double[] p)
	{
		int t,j;
		int iter = 0, max_iter=Math.max(100,k);
		double[][] Q=new double[k][k];
		double[] Qp= new double[k];
		double pQp, eps=0.005/k;
	
		for (t=0;t<k;t++)
		{
			p[t]=1.0/k;  // Valid if k = 1
			Q[t][t]=0;
			for (j=0;j<t;j++)
			{
				Q[t][t]+=r[j][t]*r[j][t];
				Q[t][j]=Q[j][t];
			}
			for (j=t+1;j<k;j++)
			{
				Q[t][t]+=r[j][t]*r[j][t];
				Q[t][j]=-r[j][t]*r[t][j];
			}
		}
		for (iter=0;iter<max_iter;iter++)
		{
			// stopping condition, recalculate QP,pQP for numerical accuracy
			pQp=0;
			for (t=0;t<k;t++)
			{
				Qp[t]=0;
				for (j=0;j<k;j++)
					Qp[t]+=Q[t][j]*p[j];
				pQp+=p[t]*Qp[t];
			}
			double max_error=0;
			for (t=0;t<k;t++)
			{
				double error=Math.abs(Qp[t]-pQp);
				if (error>max_error)
					max_error=error;
			}
			if (max_error<eps) break;
		
			for (t=0;t<k;t++)
			{
				double diff=(-Qp[t]+pQp)/Q[t][t];
				p[t]+=diff;
				pQp=(pQp+diff*(diff*Q[t][t]+2*Qp[t]))/(1+diff)/(1+diff);
				for (j=0;j<k;j++)
				{
					Qp[j]=(Qp[j]+diff*Q[t][j])/(1+diff);
					p[j]/=(1+diff);
				}
			}
		}
		if (iter>=max_iter)
			System.err.print("Exceeds max_iter in multiclass_prob\n");
	}

	// Cross-validation decision values for probability estimates
	private static void svm_binary_svc_probability(svm_problem prob, svm_parameter param, double Cp, double Cn, double[] probAB)
	{
		int i;
		int nr_fold = 5;
		int[] perm = new int[prob.l];
		double[] dec_values = new double[prob.l];

		// random shuffle
		for(i=0;i<prob.l;i++) perm[i]=i;
		for(i=0;i<prob.l;i++)
		{
			int j = i+(int)(Math.random()*(prob.l-i));
			do {int _=perm[i]; perm[i]=perm[j]; perm[j]=_;} while(false);
		}
		for(i=0;i<nr_fold;i++)
		{
			int begin = i*prob.l/nr_fold;
			int end = (i+1)*prob.l/nr_fold;
			int j,k;
			svm_problem subprob = new svm_problem();

			subprob.l = prob.l-(end-begin);
			subprob.x = new svm_node[subprob.l][];
			subprob.y = new double[subprob.l];
			
			k=0;
			for(j=0;j<begin;j++)
			{
				subprob.x[k] = prob.x[perm[j]];
				subprob.y[k] = prob.y[perm[j]];
				++k;
			}
			for(j=end;j<prob.l;j++)
			{
				subprob.x[k] = prob.x[perm[j]];
				subprob.y[k] = prob.y[perm[j]];
				++k;
			}
			int p_count=0,n_count=0;
			for(j=0;j<k;j++)
				if(subprob.y[j]>0)
					p_count++;
				else
					n_count++;
			
			if(p_count==0 && n_count==0)
				for(j=begin;j<end;j++)
					dec_values[perm[j]] = 0;
			else if(p_count > 0 && n_count == 0)
				for(j=begin;j<end;j++)
					dec_values[perm[j]] = 1;
			else if(p_count == 0 && n_count > 0)
				for(j=begin;j<end;j++)
					dec_values[perm[j]] = -1;
			else
			{
				svm_parameter subparam = (svm_parameter)param.clone();
				subparam.probability=0;
				subparam.C=1.0;
				subparam.nr_weight=2;
				subparam.weight_label = new int[2];
				subparam.weight = new double[2];
				subparam.weight_label[0]=+1;
				subparam.weight_label[1]=-1;
				subparam.weight[0]=Cp;
				subparam.weight[1]=Cn;
				svm_model submodel = svm_train(subprob,subparam);
				for(j=begin;j<end;j++)
				{
					double[] dec_value=new double[1];
					svm_predict_values(submodel,prob.x[perm[j]],dec_value);
					dec_values[perm[j]]=dec_value[0];
					// ensure +1 -1 order; reason not using CV subroutine
					dec_values[perm[j]] *= submodel.label[0];
				}		
			}
		}		
		sigmoid_train(prob.l,dec_values,prob.y,probAB);
	}

	// Return parameter of a Laplace distribution 
	private static double svm_svr_probability(svm_problem prob, svm_parameter param)
	{
		int i;
		int nr_fold = 5;
		double[] ymv = new double[prob.l];
		double mae = 0;

		svm_parameter newparam = (svm_parameter)param.clone();
		newparam.probability = 0;
		svm_cross_validation(prob,newparam,nr_fold,ymv);
		for(i=0;i<prob.l;i++)
		{
			ymv[i]=prob.y[i]-ymv[i];
			mae += Math.abs(ymv[i]);
		}		
		mae /= prob.l;
		double std=Math.sqrt(2*mae*mae);
		int count=0;
		mae=0;
		for(i=0;i<prob.l;i++)
			if (Math.abs(ymv[i]) > 5*std) 
				count=count+1;
			else 
				mae+=Math.abs(ymv[i]);
		mae /= (prob.l-count);
		System.err.print("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma="+mae+"\n");
		return mae;
	}

	// label: label name, start: begin of each class, count: #data of classes, perm: indices to the original data
	// perm, length l, must be allocated before calling this subroutine
	private static void svm_group_classes(svm_problem prob, int[] nr_class_ret, int[][] label_ret, int[][] start_ret, int[][] count_ret, int[] perm)
	{
		int l = prob.l;
		int max_nr_class = 16;
		int nr_class = 0;
		int[] label = new int[max_nr_class];
		int[] count = new int[max_nr_class];
		int[] data_label = new int[l];
		int i;

		for(i=0;i<l;i++)
		{
			int this_label = (int)(prob.y[i]);
			int j;
			for(j=0;j<nr_class;j++)
			{
				if(this_label == label[j])
				{
					++count[j];
					break;
				}
			}
			data_label[i] = j;
			if(j == nr_class)
			{
				if(nr_class == max_nr_class)
				{
					max_nr_class *= 2;
					int[] new_data = new int[max_nr_class];
					System.arraycopy(label,0,new_data,0,label.length);
					label = new_data;
					new_data = new int[max_nr_class];
					System.arraycopy(count,0,new_data,0,count.length);
					count = new_data;					
				}
				label[nr_class] = this_label;
				count[nr_class] = 1;
				++nr_class;
			}
		}

		int[] start = new int[nr_class];
		start[0] = 0;
		for(i=1;i<nr_class;i++)
			start[i] = start[i-1]+count[i-1];
		for(i=0;i<l;i++)
		{
			perm[start[data_label[i]]] = i;
			++start[data_label[i]];
		}
		start[0] = 0;
		for(i=1;i<nr_class;i++)
			start[i] = start[i-1]+count[i-1];

		nr_class_ret[0] = nr_class;
		label_ret[0] = label;
		start_ret[0] = start;
		count_ret[0] = count;
	}

	//
	// Interface functions
	//
	public static svm_model svm_train(svm_problem prob, svm_parameter param)
	{
		svm_model model = new svm_model();
		model.param = param;

		if(param.svm_type == svm_parameter.ONE_CLASS ||
		   param.svm_type == svm_parameter.EPSILON_SVR ||
		   param.svm_type == svm_parameter.NU_SVR)
		{
			// regression or one-class-svm
			model.nr_class = 2;
			model.label = null;
			model.nSV = null;
			model.probA = null; model.probB = null;
			model.sv_coef = new double[1][];

			if(param.probability == 1 &&
			   (param.svm_type == svm_parameter.EPSILON_SVR ||
			    param.svm_type == svm_parameter.NU_SVR))
			{
				model.probA = new double[1];
				model.probA[0] = svm_svr_probability(prob,param);
			}

			decision_function f = svm_train_one(prob,param,0,0);
			model.rho = new double[1];
			model.rho[0] = f.rho;

			int nSV = 0;
			int i;
			for(i=0;i<prob.l;i++)
				if(Math.abs(f.alpha[i]) > 0) ++nSV;
			model.l = nSV;
			model.SV = new svm_node[nSV][];
			model.sv_coef[0] = new double[nSV];
			int j = 0;
			for(i=0;i<prob.l;i++)
				if(Math.abs(f.alpha[i]) > 0)
				{
					model.SV[j] = prob.x[i];
					model.sv_coef[0][j] = f.alpha[i];
					++j;
				}
		}
		else
		{
			// classification
			int l = prob.l;
			int[] tmp_nr_class = new int[1];
			int[][] tmp_label = new int[1][];
			int[][] tmp_start = new int[1][];
			int[][] tmp_count = new int[1][];			
			int[] perm = new int[l];

			// group training data of the same class
			svm_group_classes(prob,tmp_nr_class,tmp_label,tmp_start,tmp_count,perm);
			int nr_class = tmp_nr_class[0];			
			int[] label = tmp_label[0];
			int[] start = tmp_start[0];
			int[] count = tmp_count[0];
			svm_node[][] x = new svm_node[l][];
			int i;
			for(i=0;i<l;i++)
				x[i] = prob.x[perm[i]];

			// calculate weighted C

			double[] weighted_C = new double[nr_class];
			for(i=0;i<nr_class;i++)
				weighted_C[i] = param.C;
			for(i=0;i<param.nr_weight;i++)
			{
				int j;
				for(j=0;j<nr_class;j++)
					if(param.weight_label[i] == label[j])
						break;
				if(j == nr_class)
					System.err.print("warning: class label "+param.weight_label[i]+" specified in weight is not found\n");
				else
					weighted_C[j] *= param.weight[i];
			}

			// train k*(k-1)/2 models

			boolean[] nonzero = new boolean[l];
			for(i=0;i<l;i++)
				nonzero[i] = false;
			decision_function[] f = new decision_function[nr_class*(nr_class-1)/2];

			double[] probA=null,probB=null;
			if (param.probability == 1)
			{
				probA=new double[nr_class*(nr_class-1)/2];
				probB=new double[nr_class*(nr_class-1)/2];
			}

			int p = 0;
			for(i=0;i<nr_class;i++)
				for(int j=i+1;j<nr_class;j++)
				{
					svm_problem sub_prob = new svm_problem();
					int si = start[i], sj = start[j];
					int ci = count[i], cj = count[j];
					sub_prob.l = ci+cj;
					sub_prob.x = new svm_node[sub_prob.l][];
					sub_prob.y = new double[sub_prob.l];
					int k;
					for(k=0;k<ci;k++)
					{
						sub_prob.x[k] = x[si+k];
						sub_prob.y[k] = +1;
					}
					for(k=0;k<cj;k++)
					{
						sub_prob.x[ci+k] = x[sj+k];
						sub_prob.y[ci+k] = -1;
					}

					if(param.probability == 1)
					{
						double[] probAB=new double[2];
						svm_binary_svc_probability(sub_prob,param,weighted_C[i],weighted_C[j],probAB);
						probA[p]=probAB[0];
						probB[p]=probAB[1];
					}

					f[p] = svm_train_one(sub_prob,param,weighted_C[i],weighted_C[j]);
					for(k=0;k<ci;k++)
						if(!nonzero[si+k] && Math.abs(f[p].alpha[k]) > 0)
							nonzero[si+k] = true;
					for(k=0;k<cj;k++)
						if(!nonzero[sj+k] && Math.abs(f[p].alpha[ci+k]) > 0)
							nonzero[sj+k] = true;
					++p;
				}

			// build output

			model.nr_class = nr_class;

			model.label = new int[nr_class];
			for(i=0;i<nr_class;i++)
				model.label[i] = label[i];

			model.rho = new double[nr_class*(nr_class-1)/2];
			for(i=0;i<nr_class*(nr_class-1)/2;i++)
				model.rho[i] = f[i].rho;

			if(param.probability == 1)
			{
				model.probA = new double[nr_class*(nr_class-1)/2];
				model.probB = new double[nr_class*(nr_class-1)/2];
				for(i=0;i<nr_class*(nr_class-1)/2;i++)
				{
					model.probA[i] = probA[i];
					model.probB[i] = probB[i];
				}
			}
			else
			{
				model.probA=null;
				model.probB=null;
			}

			int nnz = 0;
			int[] nz_count = new int[nr_class];
			model.nSV = new int[nr_class];
			for(i=0;i<nr_class;i++)
			{
				int nSV = 0;
				for(int j=0;j<count[i];j++)
					if(nonzero[start[i]+j])
					{
						++nSV;
						++nnz;
					}
				model.nSV[i] = nSV;
				nz_count[i] = nSV;
			}

			System.out.print("Total nSV = "+nnz+"\n");

			model.l = nnz;
			model.SV = new svm_node[nnz][];
			p = 0;
			for(i=0;i<l;i++)
				if(nonzero[i]) model.SV[p++] = x[i];

			int[] nz_start = new int[nr_class];
			nz_start[0] = 0;
			for(i=1;i<nr_class;i++)
				nz_start[i] = nz_start[i-1]+nz_count[i-1];

			model.sv_coef = new double[nr_class-1][];
			for(i=0;i<nr_class-1;i++)
				model.sv_coef[i] = new double[nnz];

			p = 0;
			for(i=0;i<nr_class;i++)
				for(int j=i+1;j<nr_class;j++)
				{
					// classifier (i,j): coefficients with
					// i are in sv_coef[j-1][nz_start[i]...],
					// j are in sv_coef[i][nz_start[j]...]

					int si = start[i];
					int sj = start[j];
					int ci = count[i];
					int cj = count[j];

					int q = nz_start[i];
					int k;
					for(k=0;k<ci;k++)
						if(nonzero[si+k])
							model.sv_coef[j-1][q++] = f[p].alpha[k];
					q = nz_start[j];
					for(k=0;k<cj;k++)
						if(nonzero[sj+k])
							model.sv_coef[i][q++] = f[p].alpha[ci+k];
					++p;
				}
		}
		return model;
	}
	
	// Stratified cross validation
	public static void svm_cross_validation(svm_problem prob, svm_parameter param, int nr_fold, double[] target)
	{
		int i;
		int[] fold_start = new int[nr_fold+1];
		int l = prob.l;
		int[] perm = new int[l];
		
		// stratified cv may not give leave-one-out rate
		// Each class to l folds -> some folds may have zero elements
		if((param.svm_type == svm_parameter.C_SVC ||
		    param.svm_type == svm_parameter.NU_SVC) && nr_fold < l)
		{
			int[] tmp_nr_class = new int[1];
			int[][] tmp_label = new int[1][];
			int[][] tmp_start = new int[1][];
			int[][] tmp_count = new int[1][];

			svm_group_classes(prob,tmp_nr_class,tmp_label,tmp_start,tmp_count,perm);

			int nr_class = tmp_nr_class[0];
			int[] label = tmp_label[0];
			int[] start = tmp_start[0];
			int[] count = tmp_count[0]; 		

			// random shuffle and then data grouped by fold using the array perm
			int[] fold_count = new int[nr_fold];
			int c;
			int[] index = new int[l];
			for(i=0;i<l;i++)
				index[i]=perm[i];
			for (c=0; c<nr_class; c++)
				for(i=0;i<count[c];i++)
				{
					int j = i+(int)(Math.random()*(count[c]-i));
					do {int _=index[start[c]+j]; index[start[c]+j]=index[start[c]+i]; index[start[c]+i]=_;} while(false);
				}
			for(i=0;i<nr_fold;i++)
			{
				fold_count[i] = 0;
				for (c=0; c<nr_class;c++)
					fold_count[i]+=(i+1)*count[c]/nr_fold-i*count[c]/nr_fold;
			}
			fold_start[0]=0;
			for (i=1;i<=nr_fold;i++)
				fold_start[i] = fold_start[i-1]+fold_count[i-1];
			for (c=0; c<nr_class;c++)
				for(i=0;i<nr_fold;i++)
				{
					int begin = start[c]+i*count[c]/nr_fold;
					int end = start[c]+(i+1)*count[c]/nr_fold;
					for(int j=begin;j<end;j++)
					{
						perm[fold_start[i]] = index[j];
						fold_start[i]++;
					}
				}
			fold_start[0]=0;
			for (i=1;i<=nr_fold;i++)
				fold_start[i] = fold_start[i-1]+fold_count[i-1];
		}
		else
		{
			for(i=0;i<l;i++) perm[i]=i;
			for(i=0;i<l;i++)
			{
				int j = i+(int)(Math.random()*(l-i));
				do {int _=perm[i]; perm[i]=perm[j]; perm[j]=_;} while(false);
			}
			for(i=0;i<=nr_fold;i++)
				fold_start[i]=i*l/nr_fold;
		}

		for(i=0;i<nr_fold;i++)
		{
			int begin = fold_start[i];
			int end = fold_start[i+1];
			int j,k;
			svm_problem subprob = new svm_problem();

			subprob.l = l-(end-begin);
			subprob.x = new svm_node[subprob.l][];
			subprob.y = new double[subprob.l];

			k=0;
			for(j=0;j<begin;j++)
			{
				subprob.x[k] = prob.x[perm[j]];
				subprob.y[k] = prob.y[perm[j]];
				++k;
			}
			for(j=end;j<l;j++)
			{
				subprob.x[k] = prob.x[perm[j]];
				subprob.y[k] = prob.y[perm[j]];
				++k;
			}
			svm_model submodel = svm_train(subprob,param);
			if(param.probability==1 &&
			   (param.svm_type == svm_parameter.C_SVC ||
			    param.svm_type == svm_parameter.NU_SVC))
			{
				double[] prob_estimates= new double[svm_get_nr_class(submodel)];
				for(j=begin;j<end;j++)
					target[perm[j]] = svm_predict_probability(submodel,prob.x[perm[j]],prob_estimates);
			}
			else
				for(j=begin;j<end;j++)
					target[perm[j]] = svm_predict(submodel,prob.x[perm[j]]);
		}
	}

	public static int svm_get_svm_type(svm_model model)
	{
		return model.param.svm_type;
	}

	public static int svm_get_nr_class(svm_model model)
	{
		return model.nr_class;
	}

	public static void svm_get_labels(svm_model model, int[] label)
	{
		if (model.label != null)
			for(int i=0;i<model.nr_class;i++)
				label[i] = model.label[i];
	}

	public static double svm_get_svr_probability(svm_model model)
	{
		if ((model.param.svm_type == svm_parameter.EPSILON_SVR || model.param.svm_type == svm_parameter.NU_SVR) &&
		    model.probA!=null)
		return model.probA[0];
		else
		{
			System.err.print("Model doesn't contain information for SVR probability inference\n");
			return 0;
		}
	}

	public static void svm_predict_values(svm_model model, svm_node[] x, double[] dec_values)
	{
		if(model.param.svm_type == svm_parameter.ONE_CLASS ||
		   model.param.svm_type == svm_parameter.EPSILON_SVR ||
		   model.param.svm_type == svm_parameter.NU_SVR)
		{
			double[] sv_coef = model.sv_coef[0];
			double sum = 0;
			for(int i=0;i<model.l;i++)
				sum += sv_coef[i] * Kernel.k_function(x,model.SV[i],model.param);
			sum -= model.rho[0];
			dec_values[0] = sum;
		}
		else
		{
			int i;
			int nr_class = model.nr_class;
			int l = model.l;
		
			double[] kvalue = new double[l];
			for(i=0;i<l;i++)
				kvalue[i] = Kernel.k_function(x,model.SV[i],model.param);

			int[] start = new int[nr_class];
			start[0] = 0;
			for(i=1;i<nr_class;i++)
				start[i] = start[i-1]+model.nSV[i-1];

			int p=0;
			for(i=0;i<nr_class;i++)
				for(int j=i+1;j<nr_class;j++)
				{
					double sum = 0;
					int si = start[i];
					int sj = start[j];
					int ci = model.nSV[i];
					int cj = model.nSV[j];
				
					int k;
					double[] coef1 = model.sv_coef[j-1];
					double[] coef2 = model.sv_coef[i];
					for(k=0;k<ci;k++)
						sum += coef1[si+k] * kvalue[si+k];
					for(k=0;k<cj;k++)
						sum += coef2[sj+k] * kvalue[sj+k];
					sum -= model.rho[p];
					dec_values[p] = sum;					
					p++;
				}
		}
	}

	public static double svm_predict(svm_model model, svm_node[] x)
	{
		if(model.param.svm_type == svm_parameter.ONE_CLASS ||
		   model.param.svm_type == svm_parameter.EPSILON_SVR ||
		   model.param.svm_type == svm_parameter.NU_SVR)
		{
			double[] res = new double[1];
			svm_predict_values(model, x, res);

			if(model.param.svm_type == svm_parameter.ONE_CLASS)
				return (res[0]>0)?1:-1;
			else
				return res[0];
		}
		else
		{
			int i;
			int nr_class = model.nr_class;
			double[] dec_values = new double[nr_class*(nr_class-1)/2];
			svm_predict_values(model, x, dec_values);

			int[] vote = new int[nr_class];
			for(i=0;i<nr_class;i++)
				vote[i] = 0;
			int pos=0;
			for(i=0;i<nr_class;i++)
				for(int j=i+1;j<nr_class;j++)
				{
					if(dec_values[pos++] > 0)
						++vote[i];
					else
						++vote[j];
				}

			int vote_max_idx = 0;
			for(i=1;i<nr_class;i++)
				if(vote[i] > vote[vote_max_idx])
					vote_max_idx = i;
			return model.label[vote_max_idx];
		}
	}

	public static double svm_predict_probability(svm_model model, svm_node[] x, double[] prob_estimates)
	{
		if ((model.param.svm_type == svm_parameter.C_SVC || model.param.svm_type == svm_parameter.NU_SVC) &&
		    model.probA!=null && model.probB!=null)
		{
			int i;
			int nr_class = model.nr_class;
			double[] dec_values = new double[nr_class*(nr_class-1)/2];
			svm_predict_values(model, x, dec_values);

			double min_prob=1e-7;
			double[][] pairwise_prob=new double[nr_class][nr_class];
			
			int k=0;
			for(i=0;i<nr_class;i++)
				for(int j=i+1;j<nr_class;j++)
				{
					pairwise_prob[i][j]=Math.min(Math.max(sigmoid_predict(dec_values[k],model.probA[k],model.probB[k]),min_prob),1-min_prob);
					pairwise_prob[j][i]=1-pairwise_prob[i][j];
					k++;
				}
			multiclass_probability(nr_class,pairwise_prob,prob_estimates);

			int prob_max_idx = 0;
			for(i=1;i<nr_class;i++)
				if(prob_estimates[i] > prob_estimates[prob_max_idx])
					prob_max_idx = i;
			return model.label[prob_max_idx];
		}
		else 
			return svm_predict(model, x);
	}

	static final String svm_type_table[] =
	{
		"c_svc","nu_svc","one_class","epsilon_svr","nu_svr",
	};

	static final String kernel_type_table[]=
	{
		"linear","polynomial","rbf","sigmoid","precomputed"
	};

	public static void svm_save_model(String model_file_name, svm_model model) throws IOException
	{
		DataOutputStream fp = new DataOutputStream(new FileOutputStream(model_file_name));

		svm_parameter param = model.param;

		fp.writeBytes("svm_type "+svm_type_table[param.svm_type]+"\n");
		fp.writeBytes("kernel_type "+kernel_type_table[param.kernel_type]+"\n");

		if(param.kernel_type == svm_parameter.POLY)
			fp.writeBytes("degree "+param.degree+"\n");

		if(param.kernel_type == svm_parameter.POLY ||
		   param.kernel_type == svm_parameter.RBF ||
		   param.kernel_type == svm_parameter.SIGMOID)
			fp.writeBytes("gamma "+param.gamma+"\n");

		if(param.kernel_type == svm_parameter.POLY ||
		   param.kernel_type == svm_parameter.SIGMOID)
			fp.writeBytes("coef0 "+param.coef0+"\n");

		int nr_class = model.nr_class;
		int l = model.l;
		fp.writeBytes("nr_class "+nr_class+"\n");
		fp.writeBytes("total_sv "+l+"\n");
	
		{
			fp.writeBytes("rho");
			for(int i=0;i<nr_class*(nr_class-1)/2;i++)
				fp.writeBytes(" "+model.rho[i]);
			fp.writeBytes("\n");
		}
	
		if(model.label != null)
		{
			fp.writeBytes("label");
			for(int i=0;i<nr_class;i++)
				fp.writeBytes(" "+model.label[i]);
			fp.writeBytes("\n");
		}

		if(model.probA != null) // regression has probA only
		{
			fp.writeBytes("probA");
			for(int i=0;i<nr_class*(nr_class-1)/2;i++)
				fp.writeBytes(" "+model.probA[i]);
			fp.writeBytes("\n");
		}
		if(model.probB != null) 
		{
			fp.writeBytes("probB");
			for(int i=0;i<nr_class*(nr_class-1)/2;i++)
				fp.writeBytes(" "+model.probB[i]);
			fp.writeBytes("\n");
		}

		if(model.nSV != null)
		{
			fp.writeBytes("nr_sv");
			for(int i=0;i<nr_class;i++)
				fp.writeBytes(" "+model.nSV[i]);
			fp.writeBytes("\n");
		}

		fp.writeBytes("SV\n");
		double[][] sv_coef = model.sv_coef;
		svm_node[][] SV = model.SV;

		for(int i=0;i<l;i++)
		{
			for(int j=0;j<nr_class-1;j++)
				fp.writeBytes(sv_coef[j][i]+" ");

			svm_node[] p = SV[i];
			if(param.kernel_type == svm_parameter.PRECOMPUTED)
				fp.writeBytes("0:"+(int)(p[0].value));
			else	
				for(int j=0;j<p.length;j++)
					fp.writeBytes(p[j].index+":"+p[j].value+" ");
			fp.writeBytes("\n");
		}

		fp.close();
	}

	private static double atof(String s)
	{
		return Double.valueOf(s).doubleValue();
	}

	private static int atoi(String s)
	{
		return Integer.parseInt(s);
	}

	public static svm_model svm_load_model(String model_file_name) throws IOException
	{
		BufferedReader fp = new BufferedReader(new InputStreamReader(new FileInputStream(
      model_file_name), "UTF-8"));

		// read parameters

		svm_model model = new svm_model();
		svm_parameter param = new svm_parameter();
		model.param = param;
		model.rho = null;
		model.probA = null;
		model.probB = null;
		model.label = null;
		model.nSV = null;

		while(true)
		{
			String cmd = fp.readLine();
			String arg = cmd.substring(cmd.indexOf(' ')+1);

			if(cmd.startsWith("svm_type"))
			{
				int i;
				for(i=0;i<svm_type_table.length;i++)
				{
					if(arg.indexOf(svm_type_table[i])!=-1)
					{
						param.svm_type=i;
						break;
					}
				}
				if(i == svm_type_table.length)
				{
					System.err.print("unknown svm type.\n");
					return null;
				}
			}
			else if(cmd.startsWith("kernel_type"))
			{
				int i;
				for(i=0;i<kernel_type_table.length;i++)
				{
					if(arg.indexOf(kernel_type_table[i])!=-1)
					{
						param.kernel_type=i;
						break;
					}
				}
				if(i == kernel_type_table.length)
				{
					System.err.print("unknown kernel function.\n");
					return null;
				}
			}
			else if(cmd.startsWith("degree"))
				param.degree = atoi(arg);
			else if(cmd.startsWith("gamma"))
				param.gamma = atof(arg);
			else if(cmd.startsWith("coef0"))
				param.coef0 = atof(arg);
			else if(cmd.startsWith("nr_class"))
				model.nr_class = atoi(arg);
			else if(cmd.startsWith("total_sv"))
				model.l = atoi(arg);
			else if(cmd.startsWith("rho"))
			{
				int n = model.nr_class * (model.nr_class-1)/2;
				model.rho = new double[n];
				StringTokenizer st = new StringTokenizer(arg);
				for(int i=0;i<n;i++)
					model.rho[i] = atof(st.nextToken());
			}
			else if(cmd.startsWith("label"))
			{
				int n = model.nr_class;
				model.label = new int[n];
				StringTokenizer st = new StringTokenizer(arg);
				for(int i=0;i<n;i++)
					model.label[i] = atoi(st.nextToken());					
			}
			else if(cmd.startsWith("probA"))
			{
				int n = model.nr_class*(model.nr_class-1)/2;
				model.probA = new double[n];
				StringTokenizer st = new StringTokenizer(arg);
				for(int i=0;i<n;i++)
					model.probA[i] = atof(st.nextToken());					
			}
			else if(cmd.startsWith("probB"))
			{
				int n = model.nr_class*(model.nr_class-1)/2;
				model.probB = new double[n];
				StringTokenizer st = new StringTokenizer(arg);
				for(int i=0;i<n;i++)
					model.probB[i] = atof(st.nextToken());					
			}
			else if(cmd.startsWith("nr_sv"))
			{
				int n = model.nr_class;
				model.nSV = new int[n];
				StringTokenizer st = new StringTokenizer(arg);
				for(int i=0;i<n;i++)
					model.nSV[i] = atoi(st.nextToken());
			}
			else if(cmd.startsWith("SV"))
			{
				break;
			}
			else
			{
				System.err.print("unknown text in model file: ["+cmd+"]\n");
				return null;
			}
		}

		// read sv_coef and SV

		int m = model.nr_class - 1;
		int l = model.l;
		model.sv_coef = new double[m][l];
		model.SV = new svm_node[l][];

		for(int i=0;i<l;i++)
		{
			String line = fp.readLine();
			StringTokenizer st = new StringTokenizer(line," \t\n\r\f:");

			for(int k=0;k<m;k++)
				model.sv_coef[k][i] = atof(st.nextToken());
			int n = st.countTokens()/2;
			model.SV[i] = new svm_node[n];
			for(int j=0;j<n;j++)
			{
				model.SV[i][j] = new svm_node();
				model.SV[i][j].index = atoi(st.nextToken());
				model.SV[i][j].value = atof(st.nextToken());
			}
		}

		fp.close();
		return model;
	}

	public static String svm_check_parameter(svm_problem prob, svm_parameter param)
	{
		// svm_type

		int svm_type = param.svm_type;
		if(svm_type != svm_parameter.C_SVC &&
		   svm_type != svm_parameter.NU_SVC &&
		   svm_type != svm_parameter.ONE_CLASS &&
		   svm_type != svm_parameter.EPSILON_SVR &&
		   svm_type != svm_parameter.NU_SVR)
		return "unknown svm type";

		// kernel_type, degree
	
		int kernel_type = param.kernel_type;
		if(kernel_type != svm_parameter.LINEAR &&
		   kernel_type != svm_parameter.POLY &&
		   kernel_type != svm_parameter.RBF &&
		   kernel_type != svm_parameter.SIGMOID &&
		   kernel_type != svm_parameter.PRECOMPUTED)
			return "unknown kernel type";

		if(param.degree < 0)
			return "degree of polynomial kernel < 0";

		// cache_size,eps,C,nu,p,shrinking

		if(param.cache_size <= 0)
			return "cache_size <= 0";

		if(param.eps <= 0)
			return "eps <= 0";

		if(svm_type == svm_parameter.C_SVC ||
		   svm_type == svm_parameter.EPSILON_SVR ||
		   svm_type == svm_parameter.NU_SVR)
			if(param.C <= 0)
				return "C <= 0";

		if(svm_type == svm_parameter.NU_SVC ||
		   svm_type == svm_parameter.ONE_CLASS ||
		   svm_type == svm_parameter.NU_SVR)
			if(param.nu <= 0 || param.nu > 1)
				return "nu <= 0 or nu > 1";

		if(svm_type == svm_parameter.EPSILON_SVR)
			if(param.p < 0)
				return "p < 0";

		if(param.shrinking != 0 &&
		   param.shrinking != 1)
			return "shrinking != 0 and shrinking != 1";

		if(param.probability != 0 &&
		   param.probability != 1)
			return "probability != 0 and probability != 1";

		if(param.probability == 1 &&
		   svm_type == svm_parameter.ONE_CLASS)
			return "one-class SVM probability output not supported yet";
		
		// check whether nu-svc is feasible
	
		if(svm_type == svm_parameter.NU_SVC)
		{
			int l = prob.l;
			int max_nr_class = 16;
			int nr_class = 0;
			int[] label = new int[max_nr_class];
			int[] count = new int[max_nr_class];

			int i;
			for(i=0;i<l;i++)
			{
				int this_label = (int)prob.y[i];
				int j;
				for(j=0;j<nr_class;j++)
					if(this_label == label[j])
					{
						++count[j];
						break;
					}

				if(j == nr_class)
				{
					if(nr_class == max_nr_class)
					{
						max_nr_class *= 2;
						int[] new_data = new int[max_nr_class];
						System.arraycopy(label,0,new_data,0,label.length);
						label = new_data;
						
						new_data = new int[max_nr_class];
						System.arraycopy(count,0,new_data,0,count.length);
						count = new_data;
					}
					label[nr_class] = this_label;
					count[nr_class] = 1;
					++nr_class;
				}
			}

			for(i=0;i<nr_class;i++)
			{
				int n1 = count[i];
				for(int j=i+1;j<nr_class;j++)
				{
					int n2 = count[j];
					if(param.nu*(n1+n2)/2 > Math.min(n1,n2))
						return "specified nu is infeasible";
				}
			}
		}

		return null;
	}

	public static int svm_check_probability_model(svm_model model)
	{
		if (((model.param.svm_type == svm_parameter.C_SVC || model.param.svm_type == svm_parameter.NU_SVC) &&
		model.probA!=null && model.probB!=null) ||
		((model.param.svm_type == svm_parameter.EPSILON_SVR || model.param.svm_type == svm_parameter.NU_SVR) &&
		 model.probA!=null))
			return 1;
		else
			return 0;
	}
}